Abstract
High efficiency and low cost are the two main goals of desulfurization system operation optimization. Some field tests were performed on a wet desulfurization system for a certain 600 MW coal-fired power unit. By changing the factors such as the absorber entrance concentration of SO2, absorber slurry pH value, the number of slurry circulating pumps, the regularity of desulfurization efficiency in different working conditions was studied. The results indicated that the desulfurization efficiency became higher when the entrance concentration of SO2 was lower or the slurry ph value was higher. Running a pump at any load will increase the liquid–gas ratio so as the desulfurization efficiency. On the basis of field tests and the analysis of operation cost, the artificial intelligence methods were used in desulfurization system operation optimization. Firstly, BPNN models of desulfurization efficiency and booster fan current were built; secondly, an optimization model of desulfurization system operation cost was established to obtain the optimal parameters by the BBO algorithm, such as limestone slurry pH value, booster fan opening degree, liquid–gas ratio, etc. The optimal solution and data analysis showed that the proposed optimization control scheme in this chapter was effective to improve desulfurization efficiency and reduce operation cost.
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References
Long H, Zhong MH (2006) Main factors affecting the power consumption rate of wet FGD equipment for 600 MW power unit. Elec Power 39:74–77
Cao ZY, Lu JW, Shi B, Li H (2010) Experimental study on optimal adjustment of flue gas desulfurization system of 1000 MW unit. East China Elec Power 38:1615–1617
Gu JF, Mao R, Lin W (2008) Operation optimization for desulphurization systems. East China Elec Power 36:115–118
Du Q, Ma CY, Dong Y (2006) The impact of the pH value of circulating slurry on a wet flue-gas desulfuration process. J Eng Therm Energy Power 21:491–495
Liu DP, Li XW, Huang YH et al (2010) Optimization of power consumption of limestone slurry preparation based on LSSVM. Environ Eng 28:104–107
Warych J, Szymanowski M (2001) Model of the wet limestone flue gas desulfurization process for cost optimization. Ind Eng Chem Res 40:2597–2605
Warych J, Szymanowski M (2002) Optimum values of process parameters of the wet limestone flue gas desulfurization system. Chem Eng Technol 25:427–432
Shuang C (2003) MATLAB toolbox for the theory and application of the neural network. University of Science and Technology Press, Hefei, China, pp 45–60
Hippert HS, Pedreira CE, Souza RC (2001) Neural networks for short-term load forecasting. IEEE Trans Power Syst 16:44–55
Gong W, Cai Z, Ling CX (2010) DE/BBO: A hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Computing 15:645–665
Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12:702–713
Rarick R, Simon D, Villaseca FE, Vyakaranam B (2009) Biogeography-based optimization and the solution of the power flow problem. In: Proceedings of the IEEE conference on systems, man, and cyberneticsm, pp 1029–1034
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Qiao, Z., Zhang, L., Li, J., Si, F., Xu, Z. (2014). Field Tests and Optimization Operation Research of a 600 MW Power Plant WFGD. In: Xing, S., Chen, S., Wei, Z., Xia, J. (eds) Unifying Electrical Engineering and Electronics Engineering. Lecture Notes in Electrical Engineering, vol 238. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4981-2_13
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DOI: https://doi.org/10.1007/978-1-4614-4981-2_13
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